Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/mingyuan-zhang/MotionDiffuse

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
https://github.com/mingyuan-zhang/MotionDiffuse

3d-generation diffusion-model motion-generation text-driven

Last synced: about 2 months ago
JSON representation

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Awesome Lists containing this project

README

        

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model


Mingyuan Zhang1* 
Zhongang Cai1,2* 
Liang Pan1
Fangzhou Hong1
Xinying Guo1
Lei Yang2
Ziwei Liu1+


1S-Lab, Nanyang Technological University 
2SenseTime Research 


*equal contribution 
+corresponding author

play the guitar
walk sadly
walk happily
check time




This repository contains the official implementation of _MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model_.

---


[Project Page]
[arXiv]
[Video]
[Colab Demo]
[Hugging Face Demo]

## Updates

[10/2022] Add a [🤗Hugging Face Demo](https://huggingface.co/spaces/mingyuan/MotionDiffuse) for text-driven motion generation!

[10/2022] Add a [Colab Demo](https://colab.research.google.com/drive/1Dp6VsZp2ozKuu9ccMmsDjyij_vXfCYb3?usp=sharing) for text-driven motion generation! [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Dp6VsZp2ozKuu9ccMmsDjyij_vXfCYb3?usp=sharing)

[10/2022] Code release for text-driven motion generation!

[8/2022] [Paper](https://arxiv.org/abs/2208.15001) uploaded to arXiv. [![arXiv](https://img.shields.io/badge/arXiv-2208.15001-b31b1b.svg)](https://arxiv.org/abs/2208.15001)

## Text-driven Motion Generation

You may refer to [this file](text2motion/README.md) for detailed introduction.

## Citation

If you find our work useful for your research, please consider citing the paper:

```
@article{zhang2022motiondiffuse,
title={MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model},
author={Zhang, Mingyuan and Cai, Zhongang and Pan, Liang and Hong, Fangzhou and Guo, Xinying and Yang, Lei and Liu, Ziwei},
journal={arXiv preprint arXiv:2208.15001},
year={2022}
}
```

## Acknowledgements

This study is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).